Applying Machine Learning–Based Approaches Using Experimental Data to Model DC Series Arc Fault in Photovoltaic Systems

DC series arc faults (DC SAF) in photovoltaic (PV) systems can lead to electrical fires and electric shock hazards. Therefore, DC SAF modeling and detection is a significant process for ensuring the safety of PV panels and is necessary for producing PV systems in actual applications. Using real data, for the first time, this study presents a DC SAF modeling technique based on machine learning (ML) algorithms. Considering the unpredictable and nonlinear nature of such arcs and the application of ML in solving nonlinear and complex problems, multilayer perceptron, radial basis function, and support vector machine algorithms are used to model DC SAF in PV systems. The performance of proposed ML‐based approaches is compared with well‐known traditional models by using error indices, which are computed using a test data set. Finally, comprehensive evaluations and results of modeling demonstrate that proposed models based on ML methods remarkably improved modeling accuracy and generalization capability in DC SAF modeling.